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Ask your vector RAG pipeline "what are the main themes in this corpus?" and watch it return three random chunks that share a keyword. Flat vector retrieval is built for "find me th...
RAG or Retrival-Augumented Generation, is an approach that combines Large Language Model(LLM) with external data source. It enhance the…Continue reading on Medium »
Retrieval-augmented generation (RAG) has become the de facto standard for grounding large language models (LLMs) in private data. The standard architecture — chunking documents, em...
I'm building a benchmarking platform to rigorously compare three AI retrieval pipelines on a large corpus of Indian public health research papers from PubMed Central. Here's the ar...
To recall, Integrating our private documents with LLM is called RAG. Lets assume that, we have some pdfs containing our data. That data in the pdf will be broken down into chunk...
A new way to build vector RAG—structure-aware and reasoning-capable The post Proxy-Pointer RAG: Achieving Vectorless Accuracy at Vector RAG Scale and Cost appeared first on Towards...
Retrieval-augmented generation, or RAG, is a method for grounding a language model's response in external data that it didn't have access to during training. Instead of relying onl...
Юридический домен требует понимания многочисленных связей между сущностями, рассеянными по множеству документов. Поэтому кажется, что область знаний, организованная таким образом,...
A scalable semantic localization layer for entity and relationship reconciliation The post Proxy-Pointer RAG: Solving Entity and Relationship Sprawl in Large Knowledge Graphs appe...
Retrieval-Augmented Generation (RAG) has become a standard technique for grounding large language models in external knowledge — but the moment you move beyond plain text and sta...
Traditional RAG evaluation relies on human-annotated "standard answers," but in the GraphRAG era, this approach is losing its relevance. What Is a Gold Answer? A Gold...
A practical walkthrough of embedding models, vector stores, retrieval strategies, prompt engineering, and evaluation — without leaving the JVM. 1. Why RAG — and Why Java Developers...
SciGraph показывает, почему GraphRAG для научных статей — это не только про графы и LLM, но и про честные метрики. В статье — разбор системы, которая связывает PDF, авторов, методы...
You have a knowledge base full of PDFs. Someone asks: "What do you know about RAG?" Your RAG system dutifully searches all the documents, retrieves 10 passages, stuffs them into th...
Retrieval is where most RAG systems quietly break. Traditional pipelines rely on vector similarity—embedding queries and document chunks into the same space and fetching the “close...
Open source. 5-minute setup. Vector RAG done right—try it yourself. The post Proxy-Pointer RAG: Structure Meets Scale at 100% Accuracy with Smarter Retrieval appeared first on Towa...
When semantic search isn't enough for the RAG The post Hybrid Search and Re-Ranking in Production RAG appeared first on Towards Data Science.
Here is the uncomfortable truth: most teams shipping “RAG-powered” features today are over-engineering their stack.Continue reading on Medium »
Your RAG system isn’t failing at retrieval — it’s failing at reasoning. This article shows how I built a lightweight self-healing layer that detects and corrects hallucinations bef...
Retrieval-augmented generation (RAG) caught on fast — and for good reason. Connecting a large language model to your organization's documents feels like the most natural way to bui...
Попытки заменить чем-то векторный поиск в RAG продолжаются. Про GraphRAG я уже высказывался, новый претендент на замену - Pageindex.Идея простая. Сегментируем документ на страницы,...
Что такое RAG-система? Retrieval-Augmented Generation — «генерация, дополненная извлечением»: так называют архитектурный подход, при котором модель усиливает ответы, динамично допо...
In this article, I will attempt to explain why retrieval-agumented generation (RAG) fails when retrieval is treated as a one-size-fits-all approach. For example, the internal AI as...
Large language models generate fluent text. They fail to meet grounding, traceability, freshness, and access control requirements. Retrieval-augmented generation (RAG) addresses th...
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